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A "Dynamic Information Framework," as the engine for decision support

Module Framework

DIF Framework

The core of a modeling environment is built by progressive information layers identified as the required inputs for the geospatial hydrology and landscape models, (Figure 1) but can serve multiple purposes. For example, information on land cover classifications identify the biophysical attributes of vegetation needed for modeling can provide the basis for carbon inventories, regional zoning, and so on. The first layer of information is provided by topography, which defines the boundaries of a river basin. These data can be derived in many ways, from local maps to the Shuttle Radar Topography Mission (SRTM). The topography data is used to derive river networks, and grids about how flow is accumulated. Political boundaries can be superimposed on the basin, recognizing that such boundaries most frequently do not correspond to the basin itself, and leading to transboundary issues. Information on soils is needed, including soil type, depth, texture, and fertility. Such data are typically derived from local knowledge, or from global datasets. Land cover information, from regional surveys and different satellites, is critical for multiple purposes. An all-important “driver” of the land surface is climate, expressed as the minimum and maximum temperate, precipitation, and winds. These data can be derived from local weather station networks, and from regional and global data assimilation schemes and climate models.

Establishing the process to actually execute such models is not a trival process, for several reasons. The information required comes from multiple sources, from individual rain gauges to statistics on hydropower and grain yields, to glacier melting to rock types. The information required comes from multiple disciplines, which presents problems with even communication between specialists. Existing data holdings are not always readily obtainable, sometimes for institutional reasons. New field measurements, especially holistic and cross-boundaries, are challenging. Handling such diverse data and executing models is not straightforward. There are very real problems in converting data streams into useful information that go beyond a database. Perhaps most challenging is how to not only create such information, but how to get it into the hands of users of different levels, from the specialist to the local and regional decision makers to the local farmer or fisherman.